System

Abstract

An object of the system according to the embodiment is to propose an optimal practice or program based on the characteristics or future desires of a child.SOLUTION: A system according to an embodiment includes a data collection unit, a database creation unit, an information input unit, an analysis unit, a recommendation unit, a program creation unit, and an advice unit. A data collection part collects attributes, careers, characters and choices from general people to celebrities from data on a network and actual questionnaires. The database creation unit creates a database of the data collected by the data collection unit. The information input unit inputs information about the child. The analysis unit analyzes the information input by the information input unit. The recommendation unit recommends a practice based on the information analyzed by the analysis unit. The program creation unit creates a weekly program based on the practice recommended by the recommendation unit. The advice unit provides advice on the power and skill of the child based on the information analyzed by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has the drawback of making it difficult to suggest optimal lessons and programs based on a child's characteristics and future aspirations.

[0005] The system according to the embodiment aims to suggest the most suitable extracurricular activities and programs based on the child's characteristics and future aspirations. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, a database creation unit, an information input unit, an analysis unit, a recommendation unit, a program creation unit, and an advice unit. The data collection unit collects attributes, careers, personalities, and choices of people ranging from ordinary people to celebrities from online data and actual surveys. The database creation unit creates a database of the data collected by the data collection unit. The information input unit inputs information about the child. The analysis unit analyzes the information input by the information input unit. The recommendation unit recommends extracurricular activities based on the information analyzed by the analysis unit. The program creation unit creates a weekly program based on the extracurricular activities recommended by the recommendation unit. The advice unit provides advice about the child's abilities and skills based on the information analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can suggest the most suitable extracurricular activities and programs based on the child's characteristics and future aspirations. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The database system according to an embodiment of the present invention collects the attributes, careers, personalities, and choices of people from ordinary people to celebrities, and provides recommendations for lessons and weekly programs based on information about children. This allows the database system to provide the most suitable lessons and weekly programs for children, and to give advice on current abilities that need to be developed and skills that are lacking.

[0029] The database system according to the embodiment includes a data collection unit, a database creation unit, an information input unit, an analysis unit, a recommendation unit, a program creation unit, and an advice unit. The data collection unit collects attributes, careers, personalities, and choices of individuals ranging from ordinary people to celebrities from online data and actual surveys. For example, it analyzes publicly available information on the Internet and survey results and registers each individual's choices and careers in a database. The database creation unit creates a database of the data collected by the data collection unit. For example, it structures the collected data and stores it in a searchable format. The information input unit inputs information about a child, such as age, gender, interests, desired future career, and desired date and time. The analysis unit analyzes the information input by the information input unit. For example, it performs an analysis to suggest optimal lessons and programs based on the child's interests and future career. The recommendation unit recommends lessons based on the information analyzed by the analysis unit. For example, if a child inputs that they are "interested in sports," sports-related lessons are recommended. The program creation unit creates a weekly program based on the extracurricular activities recommended by the recommendation unit. For example, it creates an optimal schedule based on the child's desired dates and times. The advice unit provides advice regarding the child's abilities and skills based on the information analyzed by the analysis unit. For example, if a child inputs that they want to "improve their communication skills," it provides specific advice for improving their communication skills. This allows the database system according to the embodiment to provide optimal extracurricular activities and weekly programs for children and give advice regarding current abilities that need to be improved and skills that are lacking.

[0030] The data collection unit can evaluate the reliability of the collected data and register only highly reliable data in the database unit. The data collection unit, for example, uses the generation AI to develop an algorithm to evaluate the reliability of the collected data. For example, it calculates a reliability score based on the data's source and update frequency, and registers only data that meets a certain score or higher in the database. The data collection unit also builds a system to cross-check data from multiple sources to evaluate the reliability of the collected data. For example, if the same information is provided by multiple highly reliable sources, that data will be registered preferentially. Furthermore, when evaluating the reliability of data collected by the generation AI, the data collection unit compares it with past data to check for consistency. For example, information that contradicts past data will be deemed unreliable and not registered in the database. This improves data quality by registering only highly reliable data in the database.

[0031] The data collection unit updates the collected data in real time, and can always reflect the latest information in the database unit. The data collection unit, for example, builds a system that updates the data collected by the generation AI in real time. For example, it periodically scans publicly available information on the Internet and updates the database whenever new information is found. The data collection unit also uses an API to connect to external data sources to update the data collected by the generation AI in real time. For example, it automatically obtains the latest information from news sites and social media and reflects it in the database. When the data collection unit updates the data collected by the generation AI in real time, it also improves the accuracy of the data based on feedback from users. For example, it immediately reflects new information or corrections provided by users in the database. This keeps the data fresh by always reflecting the latest information in the database.

[0032] The data collection unit can automatically translate data in different languages ​​and build an international database. The data collection unit, for example, builds a system that automatically translates data in different languages ​​and builds an international database. For example, it creates a database that supports multiple languages, such as English, Japanese, and French. The data collection unit also uses an automatic translation function to translate data in different languages ​​in real time and register the data in a database. For example, it collects information in multiple languages ​​on the Internet, automatically translates it, and stores it. The data collection unit also builds a dictionary to accurately translate technical terms and industry-specific expressions when automatically translating data in different languages. For example, it accurately translates data that includes technical terms in the medical and technical fields. This makes it possible to build an international database by automatically translating data in different languages.

[0033] The data collection unit can visualize the collected data and provide a database that is visually easy to understand. The data collection unit, for example, uses generative AI to build a system that visualizes the collected data. For example, it displays data trends and patterns in graphs and charts. In addition, to visualize the collected data, the data collection unit develops a tool that visually displays the results of the data analysis by the generative AI. For example, it visualizes the distribution and correlation of data. Furthermore, the data collection unit provides an interface that allows users to intuitively understand the data based on the visualized data. For example, it creates an interactive dashboard. In this way, by visualizing the collected data, it is possible to provide a database that is visually easy to understand.

[0034] The information input unit can check the consistency of the information entered and automatically correct any inconsistencies. The information input unit, for example, uses a generation AI to build a system that checks the consistency of the information entered. For example, it checks whether basic information such as age and gender is consistent. In addition, the information input unit develops an algorithm that allows the generation AI to evaluate the consistency of data in order to check the consistency of the information entered. For example, it checks whether the entered information matches past data. In addition, the information input unit builds a system that allows the generation AI to check the consistency of the information entered and automatically correct any inconsistencies. For example, it automatically corrects incorrect information and registers the correct information in a database. In this way, the accuracy of the data is improved by checking the consistency of the information entered and automatically correcting any inconsistencies.

[0035] The analysis unit can perform a simulation of future career choices based on the information input by the information input unit and suggest an optimal career path. The analysis unit, for example, builds a system in which the generation AI performs a simulation of future career choices based on the input information. For example, it suggests an optimal career path based on the child's interests and skills. The analysis unit also develops an algorithm in which the generation AI performs a simulation of future career choices based on the input information and suggests a career path. For example, it evaluates occupational aptitude based on past data. The analysis unit also builds a system in which the generation AI analyzes the input information and performs a simulation of future career choices. For example, it suggests an optimal career path based on the child's interests and skills. In this way, the generation AI performs a simulation of future career choices and suggests an optimal career path, thereby expanding the child's future options.

[0036] The analysis unit can collect information about children from different cultural backgrounds and perform analysis that takes cultural backgrounds into consideration. The analysis unit, for example, collects information about children from different cultural backgrounds and builds a system that performs analysis that takes cultural backgrounds into consideration. For example, it registers the educational systems and cultural customs of each country in a database. The analysis unit also develops an algorithm in which the generation AI collects information about children from different cultural backgrounds and performs analysis that takes cultural backgrounds into consideration. For example, it provides analysis results that reflect cultural differences. The analysis unit also collects information about children from different cultural backgrounds and builds a multilingual database in which the generation AI performs analysis that takes cultural backgrounds into consideration. For example, it registers the educational systems and cultural customs of each country in the database. This allows the generation AI to collect information about children from different cultural backgrounds and perform analysis that takes cultural backgrounds into consideration, making it possible to make proposals from a more diverse range of perspectives.

[0037] The analysis unit can provide a tool to visualize children's interests based on the information input by the information input unit. The analysis unit, for example, uses a generative AI to develop a tool to visualize children's interests based on the input information. For example, it displays children's interests using graphs and charts. The analysis unit also provides an interface for the generative AI to visualize children's interests based on the input information. For example, it creates an interactive dashboard. The analysis unit also builds a system in which the generative AI analyzes the input information and provides a tool to visualize children's interests. For example, it visually displays data trends and patterns. This allows children's interests to be visualized for a more intuitive understanding.

[0038] The recommendation unit can evaluate the effectiveness of lessons based on past data and recommend the most effective lessons. The recommendation unit, for example, uses a generation AI to build a system that evaluates the effectiveness of lessons based on past data. For example, it analyzes the grades and feedback of past participants to identify effective lessons. The recommendation unit also develops an algorithm that uses the generation AI to evaluate the effectiveness of lessons based on past data and recommend the most effective lessons. For example, it evaluates based on the growth rate and satisfaction of participants. The recommendation unit also builds a system that uses the generation AI to analyze past data and evaluate the effectiveness of lessons. For example, it identifies and recommends effective lessons based on the grades and feedback of participants. This allows the effectiveness of lessons to be evaluated based on past data and the most effective lessons to be recommended, thereby promoting children's growth.

[0039] The program creation unit is able to propose an optimal schedule by taking into consideration the child's physical condition and fatigue level when creating a weekly program. For example, the program creation unit builds a system in which the generation AI takes into consideration the child's physical condition and fatigue level when creating a weekly program. For example, it proposes an optimal schedule based on the child's sleep data and activity level. The program creation unit also develops an algorithm in which the generation AI creates a weekly program by taking into consideration the child's physical condition and fatigue level. For example, it predicts the child's physical condition and fatigue level based on past data and adjusts the schedule. The program creation unit also builds a system in which the generation AI takes into consideration the child's physical condition and fatigue level when creating a weekly program. For example, it proposes an optimal schedule based on the child's health data. In this way, by proposing an optimal schedule that takes into consideration the child's physical condition and fatigue level, the child's health is maintained while their growth is promoted.

[0040] The recommendation unit collects information on extracurricular activities in different regions and can make recommendations that take into account the characteristics of each region. For example, the recommendation unit builds a system that collects information on extracurricular activities in different regions and makes recommendations that take into account the characteristics of each region. For example, it registers information on popular extracurricular activities and facilities in a database. The recommendation unit also develops an algorithm in which the generation AI collects information on extracurricular activities in different regions and makes recommendations that take into account the characteristics of each region. For example, it makes recommendations that reflect the culture and climate of the region. The recommendation unit also collects information on extracurricular activities in different regions and builds a multilingual database in which the generation AI makes recommendations that take into account the characteristics of each region. For example, it registers information on extracurricular activities in each region in the database. This allows the system to collect information on extracurricular activities in different regions and make recommendations that take into account the characteristics of each region, thereby suggesting extracurricular activities that are suitable for each region.

[0041] The program creation unit can visualize the weekly program and provide it in a format that is visually easy to understand. The program creation unit, for example, uses a generation AI to build a system that visualizes the weekly program. For example, the weekly program is displayed in a calendar format or graphs. In addition, in order to visualize the weekly program, the program creation unit develops a tool that visually displays the results of the data analysis by the generation AI. For example, it visualizes the program's progress and achievement level. Furthermore, the program creation unit provides an interface that allows the user to intuitively understand the program based on the visualized weekly program. For example, it creates an interactive dashboard. In this way, the weekly program can be visualized and provided in a format that is visually easy to understand.

[0042] The advice unit can analyze a child's current abilities and skills in detail and propose specific improvement measures. The advice unit, for example, uses a generation AI to build a system that analyzes a child's current abilities and skills in detail. For example, it proposes specific improvement measures based on the child's grades and activity data. In addition, the advice unit develops an algorithm that uses the generation AI to propose specific improvement measures based on the results of the data analysis in order to analyze a child's current abilities and skills in detail. For example, it proposes optimal improvement measures based on past data. In addition, the advice unit builds a system that uses the generation AI to analyze a child's current abilities and skills in detail and proposes specific improvement measures. For example, it proposes specific improvement measures based on the child's grades and activity data. In this way, by analyzing a child's current abilities and skills in detail and proposing specific improvement measures, the child's growth is promoted.

[0043] The advice unit can predict growth in strength and skills based on past data and provide future advice. For example, the advice unit builds a system in which the generation AI predicts growth in strength and skills based on past data. For example, future advice is provided based on a child's grades and activity data. The advice unit also develops an algorithm in which the generation AI predicts growth in strength and skills based on past data and provides future advice. For example, growth is predicted based on past data and optimal advice is provided. The advice unit also builds a system in which the generation AI analyzes past data and predicts growth in strength and skills. For example, future advice is provided based on a child's grades and activity data. In this way, growth in strength and skills is predicted based on past data and future advice is provided, thereby supporting a child's growth over the long term.

[0044] The advice unit can collect opinions from experts in different fields and provide comprehensive advice. For example, the advice unit builds a system that collects opinions from experts in different fields and provides comprehensive advice. For example, it registers the opinions of experts in education, sports, art, etc. in a database. The advice unit also develops an algorithm that allows the generation AI to collect opinions from experts in different fields and provide comprehensive advice. For example, it provides advice by integrating the opinions of experts in each field. The advice unit also builds a multilingual database for the generation AI to collect opinions from experts in different fields and provide comprehensive advice. For example, it registers the opinions of experts in each field in the database. This makes it possible to collect opinions from experts in different fields and provide comprehensive advice, making it possible to provide advice from a more multifaceted perspective.

[0045] The advice unit can visualize the advice and provide it in a format that is visually easy to understand. The advice unit, for example, uses the generation AI to build a system that visualizes the advice. For example, the advice is displayed using graphs or charts. In addition, in order to visualize the advice, the advice unit develops a tool that visually displays the results of the data analysis by the generation AI. For example, it visualizes the progress and achievement of the advice. Furthermore, the advice unit provides an interface that allows the user to intuitively understand the advice based on the visualized advice. For example, it creates an interactive dashboard. In this way, by visualizing the advice, it can be provided in a format that is visually easy to understand.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The data collection unit can evaluate the reliability of the collected data and register only highly reliable data in the database unit. For example, it can calculate a reliability score based on the source of the data and update frequency, and register only data that has a certain score or higher in the database. The data collection unit also builds a system to cross-check data from multiple information sources in order to evaluate the reliability of the collected data. For example, if the same information is provided by multiple highly reliable sources, it will register that data preferentially. When evaluating the reliability of collected data, the data collection unit also checks whether it is consistent by comparing it with past data. For example, information that contradicts past data will be deemed to be unreliable and will not be registered in the database. This improves data quality by registering only highly reliable data in the database.

[0048] The data collection unit updates the collected data in real time, and can always reflect the latest information in the database unit. For example, it periodically scans publicly available information on the Internet and updates the database whenever new information is found. The data collection unit also uses APIs to connect to external data sources, automatically obtaining the latest information from news sites and social media, and reflecting it in the database. The data collection unit also improves the accuracy of the data based on feedback from users. For example, new information or corrections provided by users are immediately reflected in the database. This keeps the data fresh by always reflecting the latest information in the database.

[0049] The data collection unit can automatically translate data in different languages ​​and build an international database. For example, it creates a database that supports multiple languages, such as English, Japanese, and French. The data collection unit also uses an automatic translation function to translate data in different languages ​​in real time and register it in a database. For example, it collects information in multiple languages ​​on the Internet, automatically translates it, and stores it. The data collection unit also builds a dictionary to accurately translate technical terms and industry-specific expressions. For example, it accurately translates data that includes technical terms in the medical and technical fields. This makes it possible to build an international database by automatically translating data in different languages.

[0050] The data collection unit can visualize the collected data and provide a database that is visually easy to understand. For example, it can display data trends and patterns in graphs and charts. The data collection unit also develops tools that allow the generative AI to visually display the results of data analysis. For example, it can visualize data distributions and correlations. The data collection unit also provides an interface that allows users to intuitively understand the data based on the visualized data. For example, it can create an interactive dashboard. In this way, by visualizing the collected data, it is possible to provide a database that is visually easy to understand.

[0051] The information input unit checks the consistency of the information entered and can automatically correct any inconsistencies. For example, it checks whether basic information such as age and gender is consistent. The information input unit also develops an algorithm for the generation AI to evaluate the consistency of data in order to check the consistency of the information entered. For example, it checks whether the entered information matches past data. The information input unit also builds a system for the generation AI to check the consistency of the information entered and automatically correct any inconsistencies. For example, it automatically corrects incorrect information and registers the correct information in the database. This improves the accuracy of the data by checking the consistency of the information entered and automatically correcting any inconsistencies.

[0052] The analysis unit can perform a simulation of future career choices based on the information input by the information input unit and suggest the optimal career path. For example, it can suggest the optimal career path based on the child's interests and skills. The analysis unit also develops an algorithm that allows the generation AI to perform a simulation of future career choices and suggest a career path. For example, it can evaluate occupational aptitude based on past data. The analysis unit also builds a system that allows the generation AI to analyze the information input and perform a simulation of future career choices. For example, it can suggest the optimal career path based on the child's interests and skills. This allows the generation AI to perform a simulation of future career choices and suggest the optimal career path, thereby expanding the child's future options.

[0053] The analysis unit can collect information about children from different cultural backgrounds and perform analysis that takes cultural background into consideration. For example, it registers each country's educational systems and cultural customs in a database. The analysis unit also allows the generation AI to collect information about children from different cultural backgrounds and develop algorithms that perform analysis that takes cultural background into consideration. For example, it provides analysis results that reflect cultural differences. The analysis unit also allows the generation AI to build a multilingual database in order to collect information about children from different cultural backgrounds and perform analysis that takes cultural background into consideration. For example, it registers each country's educational systems and cultural customs in a database. This allows information about children from different cultural backgrounds to be collected and analysis that takes cultural background into consideration, making it possible to make proposals from a more diverse range of perspectives.

[0054] The analysis unit can provide a tool to visualize children's interests based on the information input by the information input unit. For example, a tool to visualize children's interests based on the input information is developed using a generative AI. For example, the tool displays children's interests using graphs and charts. The analysis unit also provides an interface for the generative AI to visualize children's interests. For example, an interactive dashboard is created. The analysis unit also builds a system in which the generative AI analyzes the input information and provides a tool to visualize children's interests. For example, data trends and patterns are visually displayed. This allows children's interests to be visualized for a more intuitive understanding.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The data collection department collects the attributes, backgrounds, personalities, and choices of individuals from the general public to celebrities from online data and actual surveys. For example, they analyze publicly available information on the Internet and survey results, and register each individual's choices and background in a database. Step 2: The database creation unit creates a database of the data collected by the data collection unit, for example, by structuring the collected data and storing it in a searchable format. Step 3: The information input unit inputs information about the child, such as age, gender, interests, desired occupation in the future, and desired date and time. Step 4: The analysis unit analyzes the information input by the information input unit. For example, the analysis is performed to suggest optimal extracurricular activities and programs based on the child's interests and future career. Step 5: The recommendation unit recommends extracurricular activities based on the information analyzed by the analysis unit. For example, if a child enters that they are interested in sports, sports-related extracurricular activities will be recommended. Step 6: The program creation unit creates a weekly program based on the extracurricular activities recommended by the recommendation unit. For example, it creates an optimal schedule based on the child's preferred dates and times. Step 7: The advice unit provides advice on the child's abilities and skills based on the information analyzed by the analysis unit. For example, if a child inputs "I want to improve my communication skills," the advice unit provides specific advice on how to improve communication skills.

[0057] (Example 2) The database system according to an embodiment of the present invention collects the attributes, careers, personalities, and choices of people from ordinary people to celebrities, and provides recommendations for lessons and weekly programs based on information about children. This allows the database system to provide the most suitable lessons and weekly programs for children, and to give advice on current abilities that need to be developed and skills that are lacking.

[0058] The database system according to the embodiment includes a data collection unit, a database creation unit, an information input unit, an analysis unit, a recommendation unit, a program creation unit, and an advice unit. The data collection unit collects attributes, careers, personalities, and choices of individuals ranging from ordinary people to celebrities from online data and actual surveys. For example, it analyzes publicly available information on the Internet and survey results and registers each individual's choices and careers in a database. The database creation unit creates a database of the data collected by the data collection unit. For example, it structures the collected data and stores it in a searchable format. The information input unit inputs information about a child, such as age, gender, interests, desired future career, and desired date and time. The analysis unit analyzes the information input by the information input unit. For example, it performs an analysis to suggest optimal lessons and programs based on the child's interests and future career. The recommendation unit recommends lessons based on the information analyzed by the analysis unit. For example, if a child inputs that they are "interested in sports," sports-related lessons are recommended. The program creation unit creates a weekly program based on the extracurricular activities recommended by the recommendation unit. For example, it creates an optimal schedule based on the child's desired dates and times. The advice unit provides advice regarding the child's abilities and skills based on the information analyzed by the analysis unit. For example, if a child inputs that they want to "improve their communication skills," it provides specific advice for improving their communication skills. This allows the database system according to the embodiment to provide optimal extracurricular activities and weekly programs for children and give advice regarding current abilities that need to be improved and skills that are lacking.

[0059] The data collection unit can evaluate the reliability of the collected data and register only highly reliable data in the database unit. The data collection unit, for example, uses the generation AI to develop an algorithm to evaluate the reliability of the collected data. For example, it calculates a reliability score based on the data's source and update frequency, and registers only data that meets a certain score or higher in the database. The data collection unit also builds a system to cross-check data from multiple sources to evaluate the reliability of the collected data. For example, if the same information is provided by multiple highly reliable sources, that data will be registered preferentially. Furthermore, when evaluating the reliability of data collected by the generation AI, the data collection unit compares it with past data to check for consistency. For example, information that contradicts past data will be deemed unreliable and not registered in the database. This improves data quality by registering only highly reliable data in the database.

[0060] The data collection unit updates the collected data in real time, and can always reflect the latest information in the database unit. The data collection unit, for example, builds a system that updates the data collected by the generation AI in real time. For example, it periodically scans publicly available information on the Internet and updates the database whenever new information is found. The data collection unit also uses an API to connect to external data sources to update the data collected by the generation AI in real time. For example, it automatically obtains the latest information from news sites and social media and reflects it in the database. When the data collection unit updates the data collected by the generation AI in real time, it also improves the accuracy of the data based on feedback from users. For example, it immediately reflects new information or corrections provided by users in the database. This keeps the data fresh by always reflecting the latest information in the database.

[0061] The data collection unit can use the emotion estimation function to analyze the emotional aspects of the collected data and preferentially register data with positive emotions in the database unit. The data collection unit, for example, uses the emotion estimation function to build a system that analyzes the emotional aspects of the collected data. For example, it calculates an emotion score for text data and preferentially registers data with positive emotions in the database. Furthermore, in order to analyze the emotional aspects of the collected data, the generation AI evaluates the data using an emotion analysis algorithm. For example, it preferentially stores data with strong positive emotions. Furthermore, the data collection unit uses the emotion estimation function to analyze the emotional trends of the collected data and preferentially databases data with positive emotions. For example, it filters and stores data with high emotion scores. This improves the quality of the data by preferentially databases data with positive emotions.

[0062] The data collection unit can automatically translate data in different languages ​​and build an international database. The data collection unit, for example, builds a system that automatically translates data in different languages ​​and builds an international database. For example, it creates a database that supports multiple languages, such as English, Japanese, and French. The data collection unit also uses an automatic translation function to translate data in different languages ​​in real time and register the data in a database. For example, it collects information in multiple languages ​​on the Internet, automatically translates it, and stores it. The data collection unit also builds a dictionary to accurately translate technical terms and industry-specific expressions when automatically translating data in different languages. For example, it accurately translates data that includes technical terms in the medical and technical fields. This makes it possible to build an international database by automatically translating data in different languages.

[0063] The data collection unit can visualize the collected data and provide a database that is visually easy to understand. The data collection unit, for example, uses generative AI to build a system that visualizes the collected data. For example, it displays data trends and patterns in graphs and charts. In addition, to visualize the collected data, the data collection unit develops a tool that visually displays the results of the data analysis by the generative AI. For example, it visualizes the distribution and correlation of data. Furthermore, the data collection unit provides an interface that allows users to intuitively understand the data based on the visualized data. For example, it creates an interactive dashboard. In this way, by visualizing the collected data, it is possible to provide a database that is visually easy to understand.

[0064] The data collection unit can use the emotion estimation function to analyze the emotional trends of the collected data and filter the database based on emotions. The data collection unit, for example, uses the emotion estimation function to build a system that analyzes the emotional trends of the collected data. For example, it identifies trends based on the emotion scores of the data and filters the database. In addition, in order to analyze the emotional trends of the collected data, the generation AI evaluates the data using an emotion analysis algorithm. For example, it preferentially stores data with strong positive emotions. In addition, the data collection unit uses the emotion estimation function to analyze the emotional trends of the collected data and filter the database based on emotions. For example, it filters and stores data with high emotion scores. In this way, analyzing emotional trends and filtering the database based on emotions improves the quality of the data.

[0065] The information input unit can check the consistency of the information entered and automatically correct any inconsistencies. The information input unit, for example, uses a generation AI to build a system that checks the consistency of the information entered. For example, it checks whether basic information such as age and gender is consistent. In addition, the information input unit develops an algorithm that allows the generation AI to evaluate the consistency of data in order to check the consistency of the information entered. For example, it checks whether the entered information matches past data. In addition, the information input unit builds a system that allows the generation AI to check the consistency of the information entered and automatically correct any inconsistencies. For example, it automatically corrects incorrect information and registers the correct information in a database. In this way, the accuracy of the data is improved by checking the consistency of the information entered and automatically correcting any inconsistencies.

[0066] The analysis unit can perform a simulation of future career choices based on the information input by the information input unit and suggest an optimal career path. The analysis unit, for example, builds a system in which the generation AI performs a simulation of future career choices based on the input information. For example, it suggests an optimal career path based on the child's interests and skills. The analysis unit also develops an algorithm in which the generation AI performs a simulation of future career choices based on the input information and suggests a career path. For example, it evaluates occupational aptitude based on past data. The analysis unit also builds a system in which the generation AI analyzes the input information and performs a simulation of future career choices. For example, it suggests an optimal career path based on the child's interests and skills. In this way, the generation AI performs a simulation of future career choices and suggests an optimal career path, thereby expanding the child's future options.

[0067] The analysis unit can use the emotion estimation function to analyze the child's emotions regarding the information input by the information input unit and preferentially suggest emotionally positive options. The analysis unit, for example, uses the emotion estimation function to build a system that analyzes the child's emotions regarding the input information. For example, it preferentially suggests options that the child has positive emotions about. Furthermore, in order to analyze the child's emotions regarding the input information, the generative AI evaluates the data using an emotion analysis algorithm. For example, it preferentially suggests options that have a strong positive emotion. Furthermore, the analysis unit uses the emotion estimation function to analyze the child's emotions regarding the input information and build a system that preferentially suggests emotionally positive options. For example, it filters and suggests options with a high emotion score. In this way, the child's motivation is increased by preferentially suggesting emotionally positive options.

[0068] The analysis unit can collect information about children from different cultural backgrounds and perform analysis that takes cultural backgrounds into consideration. The analysis unit, for example, collects information about children from different cultural backgrounds and builds a system that performs analysis that takes cultural backgrounds into consideration. For example, it registers the educational systems and cultural customs of each country in a database. The analysis unit also develops an algorithm in which the generation AI collects information about children from different cultural backgrounds and performs analysis that takes cultural backgrounds into consideration. For example, it provides analysis results that reflect cultural differences. The analysis unit also collects information about children from different cultural backgrounds and builds a multilingual database in which the generation AI performs analysis that takes cultural backgrounds into consideration. For example, it registers the educational systems and cultural customs of each country in the database. This allows the generation AI to collect information about children from different cultural backgrounds and perform analysis that takes cultural backgrounds into consideration, making it possible to make proposals from a more diverse range of perspectives.

[0069] The analysis unit can provide a tool to visualize children's interests based on the information input by the information input unit. The analysis unit, for example, uses a generative AI to develop a tool to visualize children's interests based on the input information. For example, it displays children's interests using graphs and charts. The analysis unit also provides an interface for the generative AI to visualize children's interests based on the input information. For example, it creates an interactive dashboard. The analysis unit also builds a system in which the generative AI analyzes the input information and provides a tool to visualize children's interests. For example, it visually displays data trends and patterns. This allows children's interests to be visualized for a more intuitive understanding.

[0070] The analysis unit uses the emotion estimation function to analyze the parent's emotions regarding the information input by the information input unit, and can make suggestions that take into account the emotions of both the parent and the child. The analysis unit, for example, uses the emotion estimation function to build a system that analyzes the parent's emotions regarding the input information. For example, it prioritizes suggesting options that the parent has positive emotions about. In addition, to analyze the parent's emotions regarding the input information, the generation AI evaluates the data using an emotion analysis algorithm. For example, it prioritizes suggesting options that the parent has positive emotions about. In addition, the analysis unit uses the emotion estimation function to analyze the parent's emotions regarding the input information, and builds a system that makes suggestions that take into account the emotions of both the parent and the child. For example, it filters and suggests options that have high emotion scores for both the parent and the child. This makes it possible to make suggestions that are more satisfying by taking into account the emotions of both the parent and the child.

[0071] The recommendation unit can evaluate the effectiveness of lessons based on past data and recommend the most effective lessons. The recommendation unit, for example, uses a generation AI to build a system that evaluates the effectiveness of lessons based on past data. For example, it analyzes the grades and feedback of past participants to identify effective lessons. The recommendation unit also develops an algorithm that uses the generation AI to evaluate the effectiveness of lessons based on past data and recommend the most effective lessons. For example, it evaluates based on the growth rate and satisfaction of participants. The recommendation unit also builds a system that uses the generation AI to analyze past data and evaluate the effectiveness of lessons. For example, it identifies and recommends effective lessons based on the grades and feedback of participants. This allows the effectiveness of lessons to be evaluated based on past data and the most effective lessons to be recommended, thereby promoting children's growth.

[0072] The program creation unit is able to propose an optimal schedule by taking into consideration the child's physical condition and fatigue level when creating a weekly program. For example, the program creation unit builds a system in which the generation AI takes into consideration the child's physical condition and fatigue level when creating a weekly program. For example, it proposes an optimal schedule based on the child's sleep data and activity level. The program creation unit also develops an algorithm in which the generation AI creates a weekly program by taking into consideration the child's physical condition and fatigue level. For example, it predicts the child's physical condition and fatigue level based on past data and adjusts the schedule. The program creation unit also builds a system in which the generation AI takes into consideration the child's physical condition and fatigue level when creating a weekly program. For example, it proposes an optimal schedule based on the child's health data. In this way, by proposing an optimal schedule that takes into consideration the child's physical condition and fatigue level, the child's health is maintained while their growth is promoted.

[0073] The recommendation unit uses the emotion estimation function to analyze children's emotions toward extracurricular activities and can prioritize recommending emotionally positive extracurricular activities. The recommendation unit, for example, uses the emotion estimation function to build a system that analyzes children's emotions toward extracurricular activities. For example, it prioritizes recommending extracurricular activities for which children have positive emotions. In addition, to analyze children's emotions toward extracurricular activities, the generative AI evaluates data using an emotion analysis algorithm. For example, it prioritizes recommending extracurricular activities for which children have strong positive emotions. In addition, the recommendation unit uses the emotion estimation function to analyze children's emotions toward extracurricular activities and build a system that prioritizes recommending emotionally positive extracurricular activities. For example, it filters and recommends extracurricular activities with high emotion scores. In this way, by prioritizing emotionally positive extracurricular activities, children's motivation is increased.

[0074] The recommendation unit collects information on extracurricular activities in different regions and can make recommendations that take into account the characteristics of each region. For example, the recommendation unit builds a system that collects information on extracurricular activities in different regions and makes recommendations that take into account the characteristics of each region. For example, it registers information on popular extracurricular activities and facilities in a database. The recommendation unit also develops an algorithm in which the generation AI collects information on extracurricular activities in different regions and makes recommendations that take into account the characteristics of each region. For example, it makes recommendations that reflect the culture and climate of the region. The recommendation unit also collects information on extracurricular activities in different regions and builds a multilingual database in which the generation AI makes recommendations that take into account the characteristics of each region. For example, it registers information on extracurricular activities in each region in the database. This allows the system to collect information on extracurricular activities in different regions and make recommendations that take into account the characteristics of each region, thereby suggesting extracurricular activities that are suitable for each region.

[0075] The program creation unit can visualize the weekly program and provide it in a format that is visually easy to understand. The program creation unit, for example, uses a generation AI to build a system that visualizes the weekly program. For example, the weekly program is displayed in a calendar format or graphs. In addition, in order to visualize the weekly program, the program creation unit develops a tool that visually displays the results of the data analysis by the generation AI. For example, it visualizes the program's progress and achievement level. Furthermore, the program creation unit provides an interface that allows the user to intuitively understand the program based on the visualized weekly program. For example, it creates an interactive dashboard. In this way, the weekly program can be visualized and provided in a format that is visually easy to understand.

[0076] The program creation unit can use the emotion estimation function to analyze parents' emotions toward the weekly program and create a program that takes into account the emotions of both parents and children. The program creation unit, for example, uses the emotion estimation function to build a system that analyzes parents' emotions toward the weekly program. For example, it prioritizes creating programs that parents have positive emotions about. Furthermore, to analyze parents' emotions toward the weekly program, the program creation unit uses an emotion analysis algorithm to evaluate data using a generation AI. For example, it prioritizes creating programs that parents have positive emotions about. Furthermore, the program creation unit uses the emotion estimation function to analyze parents' emotions toward the weekly program and build a system that creates a program that takes into account the emotions of both parents and children. For example, it filters and creates programs that have high emotion scores for both parents and children. In this way, by creating a program that takes into account the emotions of both parents and children, it is possible to provide a program that provides greater satisfaction.

[0077] The advice unit can analyze a child's current abilities and skills in detail and propose specific improvement measures. The advice unit, for example, uses a generation AI to build a system that analyzes a child's current abilities and skills in detail. For example, it proposes specific improvement measures based on the child's grades and activity data. In addition, the advice unit develops an algorithm that uses the generation AI to propose specific improvement measures based on the results of the data analysis in order to analyze a child's current abilities and skills in detail. For example, it proposes optimal improvement measures based on past data. In addition, the advice unit builds a system that uses the generation AI to analyze a child's current abilities and skills in detail and proposes specific improvement measures. For example, it proposes specific improvement measures based on the child's grades and activity data. In this way, by analyzing a child's current abilities and skills in detail and proposing specific improvement measures, the child's growth is promoted.

[0078] The advice unit can predict growth in strength and skills based on past data and provide future advice. For example, the advice unit builds a system in which the generation AI predicts growth in strength and skills based on past data. For example, future advice is provided based on a child's grades and activity data. The advice unit also develops an algorithm in which the generation AI predicts growth in strength and skills based on past data and provides future advice. For example, growth is predicted based on past data and optimal advice is provided. The advice unit also builds a system in which the generation AI analyzes past data and predicts growth in strength and skills. For example, future advice is provided based on a child's grades and activity data. In this way, growth in strength and skills is predicted based on past data and future advice is provided, thereby supporting a child's growth over the long term.

[0079] The advice unit can use the emotion estimation function to analyze the child's emotions regarding the advice and provide emotionally positive advice preferentially. The advice unit, for example, uses the emotion estimation function to build a system that analyzes the child's emotions regarding the advice. For example, it preferentially provides advice that the child has positive emotions about. Furthermore, in order to analyze the child's emotions regarding the advice, the generation AI evaluates data using an emotion analysis algorithm. For example, it preferentially provides advice that has a strong positive emotion. Furthermore, the advice unit uses the emotion estimation function to analyze the child's emotions regarding the advice and builds a system that preferentially provides emotionally positive advice. For example, it filters and provides advice with a high emotion score. In this way, the child's motivation is increased by preferentially providing emotionally positive advice.

[0080] The advice unit can collect opinions from experts in different fields and provide comprehensive advice. For example, the advice unit builds a system that collects opinions from experts in different fields and provides comprehensive advice. For example, it registers the opinions of experts in education, sports, art, etc. in a database. The advice unit also develops an algorithm that allows the generation AI to collect opinions from experts in different fields and provide comprehensive advice. For example, it provides advice by integrating the opinions of experts in each field. The advice unit also builds a multilingual database for the generation AI to collect opinions from experts in different fields and provide comprehensive advice. For example, it registers the opinions of experts in each field in the database. This makes it possible to collect opinions from experts in different fields and provide comprehensive advice, making it possible to provide advice from a more multifaceted perspective.

[0081] The advice unit can visualize the advice and provide it in a format that is visually easy to understand. The advice unit, for example, uses the generation AI to build a system that visualizes the advice. For example, the advice is displayed using graphs or charts. In addition, in order to visualize the advice, the advice unit develops a tool that visually displays the results of the data analysis by the generation AI. For example, it visualizes the progress and achievement of the advice. Furthermore, the advice unit provides an interface that allows the user to intuitively understand the advice based on the visualized advice. For example, it creates an interactive dashboard. In this way, by visualizing the advice, it can be provided in a format that is visually easy to understand.

[0082] The advice unit uses the emotion estimation function to analyze the parent's emotions regarding the advice and can provide advice that takes into account the emotions of both the parent and the child. The advice unit, for example, uses the emotion estimation function to build a system that analyzes the parent's emotions regarding the advice. For example, it prioritizes providing advice that the parent has positive emotions about. Furthermore, in order to analyze the parent's emotions regarding the advice, the generation AI evaluates data using an emotion analysis algorithm. For example, it prioritizes providing advice that the parent has positive emotions about. Furthermore, the advice unit uses the emotion estimation function to analyze the parent's emotions regarding the advice and builds a system that provides advice that takes into account the emotions of both the parent and the child. For example, it filters and provides advice that has high emotion scores for both the parent and the child. This allows for advice that takes into account the emotions of both the parent and the child, resulting in more satisfying advice.

[0083] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0084] The data collection unit can evaluate the reliability of the collected data and register only highly reliable data in the database unit. For example, it can calculate a reliability score based on the source of the data and update frequency, and register only data that has a certain score or higher in the database. The data collection unit also builds a system to cross-check data from multiple information sources in order to evaluate the reliability of the collected data. For example, if the same information is provided by multiple highly reliable sources, it will register that data preferentially. When evaluating the reliability of collected data, the data collection unit also checks whether it is consistent by comparing it with past data. For example, information that contradicts past data will be deemed to be unreliable and will not be registered in the database. This improves data quality by registering only highly reliable data in the database.

[0085] The data collection unit updates the collected data in real time, and can always reflect the latest information in the database unit. For example, it periodically scans publicly available information on the Internet and updates the database whenever new information is found. The data collection unit also uses APIs to connect to external data sources, automatically obtaining the latest information from news sites and social media, and reflecting it in the database. The data collection unit also improves the accuracy of the data based on feedback from users. For example, new information or corrections provided by users are immediately reflected in the database. This keeps the data fresh by always reflecting the latest information in the database.

[0086] The data collection unit can automatically translate data in different languages ​​and build an international database. For example, it creates a database that supports multiple languages, such as English, Japanese, and French. The data collection unit also uses an automatic translation function to translate data in different languages ​​in real time and register it in a database. For example, it collects information in multiple languages ​​on the Internet, automatically translates it, and stores it. The data collection unit also builds a dictionary to accurately translate technical terms and industry-specific expressions. For example, it accurately translates data that includes technical terms in the medical and technical fields. This makes it possible to build an international database by automatically translating data in different languages.

[0087] The data collection unit can visualize the collected data and provide a database that is visually easy to understand. For example, it can display data trends and patterns in graphs and charts. The data collection unit also develops tools that allow the generative AI to visually display the results of data analysis. For example, it can visualize data distributions and correlations. The data collection unit also provides an interface that allows users to intuitively understand the data based on the visualized data. For example, it can create an interactive dashboard. In this way, by visualizing the collected data, it is possible to provide a database that is visually easy to understand.

[0088] The information input unit checks the consistency of the information entered and can automatically correct any inconsistencies. For example, it checks whether basic information such as age and gender is consistent. The information input unit also develops an algorithm for the generation AI to evaluate the consistency of data in order to check the consistency of the information entered. For example, it checks whether the entered information matches past data. The information input unit also builds a system for the generation AI to check the consistency of the information entered and automatically correct any inconsistencies. For example, it automatically corrects incorrect information and registers the correct information in the database. This improves the accuracy of the data by checking the consistency of the information entered and automatically correcting any inconsistencies.

[0089] The analysis unit can perform a simulation of future career choices based on the information input by the information input unit and suggest the optimal career path. For example, it can suggest the optimal career path based on the child's interests and skills. The analysis unit also develops an algorithm that allows the generation AI to perform a simulation of future career choices and suggest a career path. For example, it can evaluate occupational aptitude based on past data. The analysis unit also builds a system that allows the generation AI to analyze the information input and perform a simulation of future career choices. For example, it can suggest the optimal career path based on the child's interests and skills. This allows the generation AI to perform a simulation of future career choices and suggest the optimal career path, thereby expanding the child's future options.

[0090] The analysis unit uses the emotion estimation function to analyze the child's emotions regarding the information input by the information input unit, and can prioritize suggesting emotionally positive options. For example, it prioritizes suggesting options that the child has positive emotions about. The analysis unit also uses the generative AI to evaluate the data using an emotion analysis algorithm. For example, it prioritizes suggesting options that have a strong positive emotion. The analysis unit also uses the emotion estimation function to build a system that analyzes the child's emotions regarding the input information, and prioritizes suggesting emotionally positive options. For example, it filters and suggests options with a high emotion score. This increases the child's motivation by preferentially suggesting emotionally positive options.

[0091] The analysis unit can collect information about children from different cultural backgrounds and perform analysis that takes cultural background into consideration. For example, it registers each country's educational systems and cultural customs in a database. The analysis unit also allows the generation AI to collect information about children from different cultural backgrounds and develop algorithms that perform analysis that takes cultural background into consideration. For example, it provides analysis results that reflect cultural differences. The analysis unit also allows the generation AI to build a multilingual database in order to collect information about children from different cultural backgrounds and perform analysis that takes cultural background into consideration. For example, it registers each country's educational systems and cultural customs in a database. This allows information about children from different cultural backgrounds to be collected and analysis that takes cultural background into consideration, making it possible to make proposals from a more diverse range of perspectives.

[0092] The analysis unit can provide a tool to visualize children's interests based on the information input by the information input unit. For example, a tool to visualize children's interests based on the input information is developed using a generative AI. For example, the tool displays children's interests using graphs and charts. The analysis unit also provides an interface for the generative AI to visualize children's interests. For example, an interactive dashboard is created. The analysis unit also builds a system in which the generative AI analyzes the input information and provides a tool to visualize children's interests. For example, data trends and patterns are visually displayed. This allows children's interests to be visualized for a more intuitive understanding.

[0093] The analysis unit uses the emotion estimation function to analyze the parent's emotions regarding the information entered by the information input unit, and can make suggestions that take into account the emotions of both the parent and child. For example, it will prioritize suggestions of options that the parent has a positive emotion about. The analysis unit also uses the generative AI to evaluate the data using an emotion analysis algorithm. For example, it will prioritize suggestions of options that the parent has a positive emotion about. The analysis unit also uses the emotion estimation function to analyze the parent's emotions regarding the entered information, and builds a system that makes suggestions that take into account the emotions of both the parent and child. For example, it will filter and suggest options that have high emotion scores for both the parent and the child. This makes it possible to make suggestions that take into account the emotions of both the parent and the child, resulting in higher satisfaction.

[0094] The processing flow of the second embodiment will be briefly explained below.

[0095] Step 1: The data collection department collects the attributes, backgrounds, personalities, and choices of individuals from the general public to celebrities from online data and actual surveys. For example, they analyze publicly available information on the Internet and survey results, and register each individual's choices and background in a database. Step 2: The database creation unit creates a database of the data collected by the data collection unit, for example, by structuring the collected data and storing it in a searchable format. Step 3: The information input unit inputs information about the child, such as age, gender, interests, desired occupation in the future, and desired date and time. Step 4: The analysis unit analyzes the information input by the information input unit. For example, the analysis is performed to suggest optimal extracurricular activities and programs based on the child's interests and future career. Step 5: The recommendation unit recommends extracurricular activities based on the information analyzed by the analysis unit. For example, if a child enters that they are interested in sports, sports-related extracurricular activities will be recommended. Step 6: The program creation unit creates a weekly program based on the extracurricular activities recommended by the recommendation unit. For example, it creates an optimal schedule based on the child's preferred dates and times. Step 7: The advice unit provides advice on the child's abilities and skills based on the information analyzed by the analysis unit. For example, if a child inputs "I want to improve my communication skills," the advice unit provides specific advice on how to improve communication skills.

[0096] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0097] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0098] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0099] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0100] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0101] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0102] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0103] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0104] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0105] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0106] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0107] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0108] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0109] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0110] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0111] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0112] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0113] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0114] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0115] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0116] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0117] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0118] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0120] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0121] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0122] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0123] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0124] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0125] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0126] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0127] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0128] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0129] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0130] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0131] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0132] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0133] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0135] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0136] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0137] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0138] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0139] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0140] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0141] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0142] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0143] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0144] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0145] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0146] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0147] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0148] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0149] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0150] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0151] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0152] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0153] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0154] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0155] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0156] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0157] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0158] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0159] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0160] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0161] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0162] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0163] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. The data collection department collects information on the attributes, careers, personalities, and choices of people from the general public to celebrities from online data and actual surveys. a database creation unit that creates a database of the data collected by the data collection unit; an information input section for inputting information about a child; an analysis unit that analyzes the information input by the information input unit; a recommendation unit that recommends lessons based on the information analyzed by the analysis unit; a program creation unit that creates a weekly program based on the lessons recommended by the recommendation unit; an advice unit that provides advice regarding the child's strengths and skills based on the information analyzed by the analysis unit. A system characterized by:

2. The data collection unit The reliability of the collected data is evaluated, and only the highly reliable data is registered in the database section.

2. The system of claim 1.

3. The data collection unit The collected data is updated in real time, and the latest information is always reflected in the database section.

2. The system of claim 1.

4. The data collection unit The emotional aspects of the collected data are analyzed, and data with positive emotions is preferentially registered in the database section.

2. The system of claim 1.

5. The data collection unit Automatically translate data from different languages ​​to build an international database 2. The system of claim 1.

6. The data collection unit Visualize the collected data and provide a database that is easy to understand visually.

2. The system of claim 1.

Images